Artificial Intelligence Machine Learning 1 — Questions and Answers
Question 1: Which type of machine learning uses labeled training data to learn a mapping from inputs to outputs?
- Supervised learning (Correct answer)
- Unsupervised learning
- Reinforcement learning
- Semi-supervised learning
Correct answer: Supervised learning
Supervised learning trains on labeled input-output pairs to predict outputs for new inputs.
Question 2: What is the purpose of a validation set in machine learning?
- To train the model weights
- To tune hyperparameters and detect overfitting (Correct answer)
- To perform final evaluation of the model
- To augment the training data
Correct answer: To tune hyperparameters and detect overfitting
The validation set is used during training to tune hyperparameters and monitor for overfitting.
Question 3: Which metric is most appropriate when classes in a dataset are heavily imbalanced?
- Accuracy
- F1 Score (Correct answer)
- Mean Squared Error
- R-squared
Correct answer: F1 Score
F1 Score balances precision and recall, making it more informative than accuracy on imbalanced datasets.
Question 4: What does regularization in machine learning primarily address?
- Underfitting
- Overfitting (Correct answer)
- Class imbalance
- Data normalization
Correct answer: Overfitting
Regularization adds a penalty to the loss function to reduce model complexity and prevent overfitting.
Question 5: In a decision tree, what does 'pruning' accomplish?
- Adds more branches to improve accuracy
- Removes branches to reduce overfitting (Correct answer)
- Normalizes feature values
- Boosts minority class samples
Correct answer: Removes branches to reduce overfitting
Pruning removes branches that provide little power, simplifying the tree to improve generalization.
Question 6: What is the 'bias-variance tradeoff' in machine learning?
- The balance between model speed and accuracy
- The tension between underfitting (high bias) and overfitting (high variance) (Correct answer)
- The tradeoff between precision and recall
- The balance between training time and model size
Correct answer: The tension between underfitting (high bias) and overfitting (high variance)
The bias-variance tradeoff describes how increasing model complexity reduces bias but increases variance.
Artificial Intelligence Machine Learning 1
Which type of machine learning uses labeled training data to learn a mapping from inputs to outputs?